{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://raw.githubusercontent.com/Qinbf/tf-model-zoo/master/README_IMG/01.jpg)\n",
    "AI MOOC： **www.ai-xlab.com**  \n",
    "如果你也是AI爱好者，可以添加我的微信一起交流：**sdxxqbf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import fetch_lfw_people\n",
    "from sklearn.model_selection  import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 载入数据\n",
    "\n",
    "lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2363a536be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(lfw_people.images[6],cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1288\n",
      "50\n",
      "37\n"
     ]
    }
   ],
   "source": [
    "# 照片的数据格式\n",
    "n_samples, h, w = lfw_people.images.shape\n",
    "print(n_samples)\n",
    "print(h)\n",
    "print(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1288, 1850)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfw_people.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 3, ..., 5, 3, 5], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfw_people.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ariel Sharon', 'Colin Powell', 'Donald Rumsfeld', 'George W Bush',\n",
       "       'Gerhard Schroeder', 'Hugo Chavez', 'Tony Blair'], dtype='<U17')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_names = lfw_people.target_names\n",
    "target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = lfw_people.target_names.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(lfw_people.data, lfw_people.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(kernel='rbf', class_weight='balanced')\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.00      0.00      0.00        21\n",
      "     Colin Powell       0.19      1.00      0.32        62\n",
      "  Donald Rumsfeld       0.00      0.00      0.00        24\n",
      "    George W Bush       0.00      0.00      0.00       136\n",
      "Gerhard Schroeder       0.00      0.00      0.00        24\n",
      "      Hugo Chavez       0.00      0.00      0.00        15\n",
      "       Tony Blair       0.00      0.00      0.00        40\n",
      "\n",
      "      avg / total       0.04      0.19      0.06       322\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "print(classification_report(y_test, predictions, target_names=lfw_people.target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 100个维度\n",
    "n_components = 100\n",
    "\n",
    "pca = PCA(n_components=n_components, whiten=True).fit(lfw_people.data)\n",
    "\n",
    "x_train_pca = pca.transform(x_train)\n",
    "x_test_pca = pca.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(966, 100)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(kernel='rbf', class_weight='balanced')\n",
    "model.fit(x_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       1.00      0.67      0.80        21\n",
      "     Colin Powell       0.79      0.92      0.85        62\n",
      "  Donald Rumsfeld       0.82      0.75      0.78        24\n",
      "    George W Bush       0.88      0.96      0.92       136\n",
      "Gerhard Schroeder       0.83      0.83      0.83        24\n",
      "      Hugo Chavez       1.00      0.73      0.85        15\n",
      "       Tony Blair       0.94      0.75      0.83        40\n",
      "\n",
      "      avg / total       0.88      0.87      0.87       322\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test_pca)\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1, cache_size=200, class_weight='balanced', coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.005, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.1, 1, 5, 10, 100],\n",
    "              'gamma': [0.0005, 0.001, 0.005, 0.01], }\n",
    "model = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "model.fit(x_train_pca, y_train)\n",
    "print(model.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.69      0.86      0.77        21\n",
      "     Colin Powell       0.87      0.87      0.87        62\n",
      "  Donald Rumsfeld       0.73      0.79      0.76        24\n",
      "    George W Bush       0.95      0.92      0.94       136\n",
      "Gerhard Schroeder       0.78      0.88      0.82        24\n",
      "      Hugo Chavez       0.81      0.87      0.84        15\n",
      "       Tony Blair       0.97      0.82      0.89        40\n",
      "\n",
      "      avg / total       0.89      0.88      0.88       322\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test_pca)\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=3, cache_size=200, class_weight='balanced', coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.007, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.1, 0.6, 1, 2, 3],\n",
    "              'gamma': [0.003, 0.004, 0.005, 0.006, 0.007], }\n",
    "model = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "model.fit(x_train_pca, y_train)\n",
    "print(model.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.83      0.71      0.77        21\n",
      "     Colin Powell       0.86      0.90      0.88        62\n",
      "  Donald Rumsfeld       0.83      0.79      0.81        24\n",
      "    George W Bush       0.90      0.98      0.94       136\n",
      "Gerhard Schroeder       0.86      0.79      0.83        24\n",
      "      Hugo Chavez       0.92      0.80      0.86        15\n",
      "       Tony Blair       0.94      0.78      0.85        40\n",
      "\n",
      "      avg / total       0.89      0.89      0.88       322\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test_pca)\n",
    "print(classification_report(y_test, predictions, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MtVnr4+YI0oJy6SiJcNJM12+D+obP6j6C8/oo10Xt8g36nvY7acWlOb+brq81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\nUFgp6gOzUCgUCoVCobBS/D/mZcEjI6fnlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2363e4ea128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图，3行4列\n",
    "def plot_gallery(images, titles, h, w, n_row=3, n_col=5):\n",
    "    plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))\n",
    "    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)\n",
    "    for i in range(n_row * n_col):\n",
    "        plt.subplot(n_row, n_col, i + 1)\n",
    "        plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)\n",
    "        plt.title(titles[i], size=12)\n",
    "        plt.xticks(())\n",
    "        plt.yticks(())\n",
    "\n",
    "# 获取一张图片title\n",
    "def title(predictions, y_test, target_names, i):\n",
    "    pred_name = target_names[predictions[i]].split(' ')[-1]\n",
    "    true_name = target_names[y_test[i]].split(' ')[-1]\n",
    "    return 'predicted: %s\\ntrue:      %s' % (pred_name, true_name)\n",
    "\n",
    "# 获取所有图片title\n",
    "prediction_titles = [title(predictions, y_test, target_names, i) for i in range(len(predictions))]\n",
    "\n",
    "# 画图\n",
    "plot_gallery(x_test, prediction_titles, h, w)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
